A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China

Alberto Aleta; Qitong Hu; Jiachen Ye; Peng Ji; Yamir Moreno

Note that the generation time is thus T g = T E + T I . On the other hand, the mobility of individuals is implemented through a data-driven approach. We have obtained the number of individuals leaving each region each day, N o i , as well as the probability, p ij , of going from each region i to region j (see Materials and Methods for a thorough description of the data). Hence, at each time step, we select N o i individuals at random from within each region −excluding infected individuals in i, which are supposed to be under quarantine or hospitalized−, and distribute them across the country according to such probabilities. Note, however, that there could be exposed individuals, which are those that ultimately will bring the disease to other subpopulations. We also implemented a randomized version of the model for the mobility, in which the fraction of the population traveling from each region at each time step is 0.008 (estimated from the average fraction of individuals traveling during the first week of January 2020) and their destinations are chosen randomly with probability proportional to the population of the destination region.